The art of writing a concise and to-the-point resume is a talent that many of us haven’t mastered yet. Many a time, our key achievements are lost in long essays and in many cases, our recruiters are least impressed.
Here is where the usability of artificial intelligence comes into play. As a tool which can analyse through scores of content on the web and making valuable suggestions accordingly, more online platforms are now looking at AI for building the perfect resume.
In this article, we take a look at how AI is making this possible with the help of these powerful tools:
Resume Assistant: There are a number of AI-powered online resume makers and big names such as Microsoft and LinkedIn has partnered to release their own exclusive tools in 2018. LinkedIn’s AI-powered Resume Assistant was added to Microsoft’s Office 365 suite and uses the network’s professional pool to shape your resume.
By leveraging their millions of data, the new tool will go through millions of data to look for similar roles or aspirational roles to make a suggestion to you regarding your resume.
Further, the tool will help its users to take guidance from professional freelance resume experts through its other tool, Profinder, thus helping its users with interview techniques, career coaching, and resume writing.
“With over 15 million job applications being submitted on LinkedIn every week, finding the right way to represent your unique experience is important. By regularly updating your resume, you’ll already be one step ahead when the time comes to find your next role,” LinkedIn wrote on a blog.
Mosaic.ai: Is an AI career agent which can help its users plan your career and review the best skill and team culture matched opportunities. It also helps potential job seekers build resumes, discover the keywords, concepts, and topics you may have overlooked that you need in your resume to get interviews.
Its AI-powered solutions will help a person’s resume to have more visibility by matching the best job based on your skill and company. This is achieved with the help of NLP, which goes through certain keywords and suggest you the best job options based on the keyword identification, by doing so the company claims that it saves a person’s time by not having to go through several resumes.
The platform also uses culture match, where the agent creates an AI model which will help you identify the best-suited job for you and the jobs where you have a better chance of clearing an interview. In addition, there will soon be made available through platforms like Slack, Facebook, Google, Alexa, and other messaging services so that the users can ask questions.
Skillroads: Lets its users have a quick interview with its AI-programme and get all high priority skills defined and offers you professionally written and formatted results through its resume building process under 30 minutes.
It’s AI algorithms analyse background, get all your high priority skills defined, and, as a result. The AI builder works by entering people’s preferred jobs, then by taking their questionnaire about the users’ experience, the system would identify their strength and skills that are best suited for its users.
Apart from common features like AI resume generator, smart resume review and cover letter generator, what is unique to Skill Road is that it lets its users about job openings in Fortune 500 companies, identify the position that is suitable for you among these vacancies and polish your resume according to these positions.
Textkernel: The platform is powered by the latest developments in AI, Deep Learning, Semantic Search and Machine Learning. Apart from providing recruitment and staffing agencies, through its multilingual CV parsing, it eliminates manual data entry and allows candidates to apply via any device and enables better search results.
At the core of the platform, fundamental to its technology is resume and vacancy parsing models which are capable of extracting the most important from unstructured text.
“In our new generation of resume and vacancy parsing product, the traditional statistical parsing models are replaced with Deep Learning neural network models. The new models have achieved remarkable improvements over different languages, brought better generalisation to new data and new domains, and reduced the need for manual feature engineering,” the company wrote in a blog post.